In addition to video stylization, temporal consistency must be achieved in any video editing application. Optical flow is also widely used in these applications. Kong et al. [1]} proposed to extract temporally coherent albedo and shading from the video (intrinsic video) using optical flow. In addition, it was shown that intrinsic video was useful to improve optical flow estimation. Two different methods [2]}, [3]} were proposed for intrinsic video decomposition, where the temporal consistency was ensured by using optical flow. Litwinowicz et al. [4]} proposed to transform ordinary video into animations that had an impressionist effect. Optical flow fields were used to push brush strokes from frame to frame in a temporally consistent manner. Farbman and Lischinski [5]} proposed to stabilize tonal fluctuations in videos by using optical flow estimated at sparse locations. Bonneel et al. [6]} proposed to stabilize many image processing tasks, including stylization of color and tone and depth estimation using dense optical flow. Lai et al.  [7]} proposed a cascaded solution to address the video stability problem in a wide range of tasks by training an RNN using OFB temporal consistency loss. Most of these methods consider the local temporal consistency between the adjacent frames explicitly.
A generative adversarial network-based method was proposed to enhance the global consistency in many frames [8]}.
